[CPAC] Seed based correlation analysis output question

1

I’m running cpac the first time and would like to do the seed_based_correlation_analysis. the sca run is on but the output doesn’t give me any correlations images.
Could you please check if CPAC Pipeline Configuration YAML file correct?

---
# CPAC Pipeline Configuration YAML file
# Version 1.8.4.dev
#
# http://fcp-indi.github.io for more info.
#

# OUTPUTS AND DERIVATIVES
# -----------------------
post_processing:

  spatial_smoothing:

    # Smooth the derivative outputs.
    # Set as ['nonsmoothed'] to disable smoothing. Set as ['smoothed', 'nonsmoothed'] to get both.
    #
    # Options:
    #     ['smoothed', 'nonsmoothed']
    output: ['smoothed']

    # Tool to use for smoothing.
    # 'FSL' for FSL MultiImageMaths for FWHM provided
    # 'AFNI' for AFNI 3dBlurToFWHM for FWHM provided
    smoothing_method: ['FSL']

    # Full Width at Half Maximum of the Gaussian kernel used during spatial smoothing.
    # this is a fork point
    #   i.e. multiple kernels - fwhm: [4,6,8]
    fwhm: [4]

  z-scoring:

    # z-score standardize the derivatives. This may be needed for group-level analysis.
    # Set as ['raw'] to disable z-scoring. Set as ['z-scored', 'raw'] to get both.
    #
    # Options:
    #     ['z-scored', 'raw']
    output: ['z-scored']


timeseries_extraction:
  roi_paths_fully_specified: False

  run: On

  # Enter paths to region-of-interest (ROI) NIFTI files (.nii or .nii.gz) to be used for time-series extraction, and then select which types of analyses to run.
  # Denote which analyses to run for each ROI path by listing the names below. For example, if you wish to run Avg and SpatialReg, you would enter: '/path/to/ROI.nii.gz': Avg, SpatialReg
  # available analyses:
  #   /path/to/atlas.nii.gz: Avg, Voxel, SpatialReg
  tse_roi_paths:
    /cpac_templates/CC400.nii.gz: Avg
    /cpac_templates/aal_mask_pad.nii.gz: Avg
    /cpac_templates/CC200.nii.gz: Avg
    /cpac_templates/tt_mask_pad.nii.gz: Avg
    /cpac_templates/PNAS_Smith09_rsn10.nii.gz: SpatialReg
    /cpac_templates/ho_mask_pad.nii.gz: Avg
    /cpac_templates/rois_3mm.nii.gz: Avg
    /ndmg_atlases/label/Human/AAL_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/CAPRSC_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/DKT_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/DesikanKlein_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/HarvardOxfordcort-maxprob-thr25_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/HarvardOxfordsub-maxprob-thr25_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Juelich_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/MICCAI_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Schaefer1000_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Schaefer200_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Schaefer300_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Schaefer400_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Talairach_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Brodmann_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Desikan_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Glasser_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Slab907_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-17-liberal_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-17_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-7-liberal_space-MNI152NLin6_res-1x1x1.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-7_space-MNI152NLin6_res-1x1x1.nii.gz: Avg

  # Functional time-series and ROI realignment method: ['ROI_to_func'] or ['func_to_ROI']
  # 'ROI_to_func' will realign the atlas/ROI to functional space (fast)
  # 'func_to_ROI' will realign the functional time series to the atlas/ROI space
  #
  #     NOTE: in rare cases, realigning the ROI to the functional space may
  #           result in small misalignments for very small ROIs - please double
  #           check your data if you see issues
  realignment: 'ROI_to_func'

  connectivity_matrix:
    # Create a connectivity matrix from timeseries data

    # Options:
    #  ['AFNI', 'Nilearn', 'ndmg']
    using:
      - Nilearn
      - ndmg
    # Options:
    #  ['Pearson', 'Partial']
    # Note: These options are not configurable for ndmg, which will ignore these options
    measure:
      - Pearson
      - Partial


seed_based_correlation_analysis:

  roi_paths_fully_specified: False

  # SCA - Seed-Based Correlation Analysis
  # For each extracted ROI Average time series, CPAC will generate a whole-brain correlation map.
  # It should be noted that for a given seed/ROI, SCA maps for ROI Average time series will be the same.
  run: On

  # Enter paths to region-of-interest (ROI) NIFTI files (.nii or .nii.gz) to be used for seed-based correlation analysis, and then select which types of analyses to run.
  # Denote which analyses to run for each ROI path by listing the names below. For example, if you wish to run Avg and MultReg, you would enter: '/path/to/ROI.nii.gz': Avg, MultReg
  # available analyses:
  #   /path/to/atlas.nii.gz: Avg, DualReg, MultReg
  sca_roi_paths:
    /cpac_templates/PNAS_Smith09_rsn10.nii.gz: DualReg
    /cpac_templates/CC400.nii.gz: Avg, MultReg
    /cpac_templates/ez_mask_pad.nii.gz: Avg, MultReg
    /cpac_templates/aal_mask_pad.nii.gz: Avg, MultReg
    /cpac_templates/CC200.nii.gz: Avg, MultReg
    /cpac_templates/tt_mask_pad.nii.gz: Avg, MultReg
    /cpac_templates/ho_mask_pad.nii.gz: Avg, MultReg
    /cpac_templates/rois_3mm.nii.gz: Avg, MultReg

  # Normalize each time series before running Dual Regression SCA.
  norm_timeseries_for_DR: True


amplitude_low_frequency_fluctuation:

  # ALFF & f/ALFF
  # Calculate Amplitude of Low Frequency Fluctuations (ALFF) and fractional ALFF (f/ALFF) for all voxels.
  run: On

  # Frequency cutoff (in Hz) for the high-pass filter used when calculating f/ALFF.
  highpass_cutoff: [0.01]

  # Frequency cutoff (in Hz) for the low-pass filter used when calculating f/ALFF
  lowpass_cutoff: [0.1]


regional_homogeneity:

  # ReHo
  # Calculate Regional Homogeneity (ReHo) for all voxels.
  run: On

  # Number of neighboring voxels used when calculating ReHo
  # 7 (Faces)
  # 19 (Faces + Edges)
  # 27 (Faces + Edges + Corners)
  cluster_size: 27


voxel_mirrored_homotopic_connectivity:

  # VMHC
  # Calculate Voxel-mirrored Homotopic Connectivity (VMHC) for all voxels.
  run: On

  symmetric_registration:

    # Included as part of the 'Image Resource Files' package available on the Install page of the User Guide.
    # It is not necessary to change this path unless you intend to use a non-standard symmetric template.
    T1w_brain_template_symmetric: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_brain_symmetric.nii.gz

    # A reference symmetric brain template for resampling
    T1w_brain_template_symmetric_for_resample: $FSLDIR/data/standard/MNI152_T1_1mm_brain_symmetric.nii.gz

    # Included as part of the 'Image Resource Files' package available on the Install page of the User Guide.
    # It is not necessary to change this path unless you intend to use a non-standard symmetric template.
    T1w_template_symmetric: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_symmetric.nii.gz

    # A reference symmetric skull template for resampling
    T1w_template_symmetric_for_resample: $FSLDIR/data/standard/MNI152_T1_1mm_symmetric.nii.gz

    # Included as part of the 'Image Resource Files' package available on the Install page of the User Guide.
    # It is not necessary to change this path unless you intend to use a non-standard symmetric template.
    dilated_symmetric_brain_mask: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_brain_mask_symmetric_dil.nii.gz

    # A reference symmetric brain mask template for resampling
    dilated_symmetric_brain_mask_for_resample: $FSLDIR/data/standard/MNI152_T1_1mm_brain_mask_symmetric_dil.nii.gz


network_centrality:

  # Calculate Degree, Eigenvector Centrality, or Functional Connectivity Density.
  run: On

  # Maximum amount of RAM (in GB) to be used when calculating Degree Centrality.
  # Calculating Eigenvector Centrality will require additional memory based on the size of the mask or number of ROI nodes.
  memory_allocation:  1.0

  # Full path to a NIFTI file describing the mask. Centrality will be calculated for all voxels within the mask.
  template_specification_file:  /cpac_templates/Mask_ABIDE_85Percent_GM.nii.gz

  degree_centrality:

    # Enable/Disable degree centrality by selecting the connectivity weights
    #   weight_options: ['Binarized', 'Weighted']
    # disable this type of centrality with:
    #   weight_options: []
    weight_options:  ['Binarized', 'Weighted']

    # Select the type of threshold used when creating the degree centrality adjacency matrix.
    # options:
    #   'Significance threshold', 'Sparsity threshold', 'Correlation threshold'
    correlation_threshold_option: 'Sparsity threshold'

    # Based on the Threshold Type selected above, enter a Threshold Value.
    # P-value for Significance Threshold
    # Sparsity value for Sparsity Threshold
    # Pearson's r value for Correlation Threshold
    correlation_threshold: 0.001

  eigenvector_centrality:

    # Enable/Disable eigenvector centrality by selecting the connectivity weights
    #   weight_options: ['Binarized', 'Weighted']
    # disable this type of centrality with:
    #   weight_options: []
    weight_options: ['Weighted']

    # Select the type of threshold used when creating the eigenvector centrality adjacency matrix.
    # options:
    #   'Significance threshold', 'Sparsity threshold', 'Correlation threshold'
    correlation_threshold_option: 'Sparsity threshold'

    # Based on the Threshold Type selected above, enter a Threshold Value.
    # P-value for Significance Threshold
    # Sparsity value for Sparsity Threshold
    # Pearson's r value for Correlation Threshold
    correlation_threshold: 0.001

  local_functional_connectivity_density:

    # Enable/Disable lFCD by selecting the connectivity weights
    #   weight_options: ['Binarized', 'Weighted']
    # disable this type of centrality with:
    #   weight_options: []
    weight_options: ['Binarized', 'Weighted']

    # Select the type of threshold used when creating the lFCD adjacency matrix.
    # options:
    #   'Significance threshold', 'Correlation threshold'
    correlation_threshold_option: 'Correlation threshold'

    # Based on the Threshold Type selected above, enter a Threshold Value.
    # P-value for Significance Threshold
    # Sparsity value for Sparsity Threshold
    # Pearson's r value for Correlation Threshold
    correlation_threshold: 0.6


# PACKAGE INTEGRATIONS
# --------------------
PyPEER:

  # Training of eye-estimation models. Commonly used for movies data/naturalistic viewing.
  run: Off

  # PEER scan names to use for training
  # Example: ['peer_run-1', 'peer_run-2']
  eye_scan_names: []

  # Naturalistic viewing data scan names to use for eye estimation
  # Example: ['movieDM']
  data_scan_names: []

  # Template-space eye mask
  eye_mask_path: $FSLDIR/data/standard/MNI152_T1_${func_resolution}_eye_mask.nii.gz

  # PyPEER Stimulus File Path
  # This is a file describing the stimulus locations from the calibration sequence.
  stimulus_path: None

  minimal_nuisance_correction:

    # PyPEER Minimal nuisance regression
    # Note: PyPEER employs minimal preprocessing - these choices do not reflect what runs in the main pipeline.
    #       PyPEER uses non-nuisance-regressed data from the main pipeline.

    # Global signal regression (PyPEER only)
    peer_gsr: True

    # Motion scrubbing (PyPEER only)
    peer_scrub: False

    # Motion scrubbing threshold (PyPEER only)
    scrub_thresh: 0.2
2

Hi Hakimo,

Sorry you’re hitting this bug, and thanks for reporting.

I believe the issue is the same bug I describe here, which is fixed in C-PAC v1.8.5, which will be released soon. I’ll post a link here when the release with the fix is out.

1 Like
3

Thank you very much.
I hope this will be fixed in the coming latest release C-PAC v1.8.5

4

Hi, any update about latest release C-PAC v1.8.5 ?

Thanks.

5

Apologies for leaving this thread hanging for months past the release of C-PAC 1.8.5 ― there’s an unresolved error in testing the pip-installable cpac package, so the 1.8.5-compatible version (v0.6.0) hasn’t been released, though you can install the development version of the wrapper (if you use the wrapper) directly from GitHub like

pip install git+https://github.com/FCP-INDI/cpac.git@main

If you use C-PAC through Docker or Apptainer / Singularity directly, 1.8.5 should already work for you as expected, and this issue should be resolved.

― What are the expected outputs of dual regression preprocessing? - #3 by shnizzedy